Wireless sensor networks are being extensively used to study various aspects of the physical environment which are complex in nature. They are deployed for a wide range of applications such as environmental monitoring, location tracking in retail chains, gathering military intelligence, providing disaster relief, factory instrumentation, hospital management and information tracking, and the like. Many of these applications require the sensing of location of individual nodes.
The technique of wireless localization, for estimating the position of a mobile wireless node, is an area that has attracted much attention in recent years. The following papers disclose this in detail:
“A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks”; Proc. IEEE INFOCOM 2004, 2004; P. Krishnan, A. S. Krishnakumar, W. H. Ju, C. Mallows, and S. Ganu,
“Locationing in distributed ad-hoc wireless sensor networks”; IEEE International Conference on Acoustics, Speech and Signal Processing, 2001, Salt Lake City, Utah, Volume: 4, Page(s): 2037-2040, May, 2001; C. Savarese, J. M. Rabaey, J. Beutel
“A statistical modeling versus geometrical determination location approach for static positioning in indoor environment”; Proceedings of the International Symposium on Wireless Personal Multimedia Communications (WPMC '05), Aalborg, Denmark, September 2005; R. Singh, L. Macchi, and C. S. Regazzoni
“An In-Building RF-based User Location and Tracking System”; INFOCOM (2) (March 2000) pp. 775-784; Paramvir Bahl, Venkata N. Padmanabhan, RADAR
“Design and Calibration of the SpotON Ad-Hoc Location Sensing System”; August 2001; Jeffrey Hightower, Chris Vakili, Gaetano Borriello, and Roy Want
The most popular system, GPS as disclosed in “Special Issue on GPS: The Global Positioning System”; Proceedings of the IEEE, Volume 87, Number 1, pp. 3-172, January 1999; Per Enge, Pretap Misra, uses radio time-of-flight lateration via satellites, but has the limitation of only working outdoors.
Localization also done using sound as disclosed in “The cricket location-support system”; 6th ACM International Conference on Mobile Computing and Networking, August 2000; IEEE Communications Society/WCNC 2005 2353 0-7803-8966-2/05; N. B. Priyantha, A. Chakraborty, and H. Balakrishnan, using infrared as disclosed in “The Active Badge Location System”; ACM Transactions on Information Systems, Vol. 40, No. 1, pp. 91-102, January 1992; Roy Want, Andy Hopper, Veronica Falcao, Jon Gibbons, and using radio frequency identification (RFID) as disclosed in “Landmarc: Indoor location sensing using active RFID” in First IEEE International Conference on Pervasive Computing and Communications, March 2003, pg. 407; L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, relies on specialized hardwares and infrastructures which, in turn, incur additional costs. This will prohibit the use of such schemes in low-cost sensor nodes.
Localization is also done using radio interferometry as disclosed in U.S. Pat. No. 7,558,583 in which the phase offsets of the interference signal received by two receivers are measured. But here the sources of errors like multipath fading, antenna orientation, signal processing errors are more.
A very popular distance based single hop localization technique is trilateration as disclosed in “Demonstrating the effects of multi-path propagation and advantages of diversity antenna techniques”; Proc. IEEE Ant. Propag. Symposium, 2003; K. S. Bialkowski, A. Postula, is a method to find the position of an object based on distance measurements to three objects with known positions. Single-hop localization algorithms can be used in indoor and small scale outdoor applications, however, this approach is not scalable and requires the topology of the network to cover a very limited area and requires precise range measurements. As the density of nodes decrease, measurement errors increase.
Perhaps, the most important criterion of a successful location estimation technique is the accuracy of the model. Thus the quality indicators of the deployed system are reliability and the error of estimate (in percentage terms) in the given area of operation.
One of the known methods for such estimation is one using received signal strength indicator (RSSI). RSSI based localization systems are simple and inexpensive and can be used for indoor environments for estimating the locations. It is known that RSSI based localization algorithms suffer from deleterious effects of severe multipath phenomenon in indoor environments. Elnahrawy et al as disclosed in “The limits of localization using signal strength: A comparative study”; Proc. IEEE SECON 2004, 2004. [6] Kamin Whitehouse, David Culler, Macro-Calibration in Sensor/Actuator Networks, Mobile Networks and Applications, Kluwer Academic Publishers 2003, have investigated the fundamental limits of localization for wireless sensor networks using received signal strength.
The theoretical lower bounds on location estimation error (Cramer-Rao bound) using RSSI has been derived in “Using proximity and quantized RSS for sensor”, Proc. of 2nd ACM Int. Conf. on Wireless Sensor Network, 2003; N. Patwari and A. O. Hero III. Roos et al as disclosed in “A statistical modeling approach to location estimation”; IEEE Trans. Mobile Computing, 1(1), 2002, 59-69, presented a statistical modeling framework, which enables location estimation based on statistical power model.
The above discussion indicates that the RSSI based indoor localization is highly researched and the roadmap to future research indicates the requirement of an accurate model which can enable localization with precision and minimum efforts in deployment and measurements. For majority of instances, the investigators have based their models only on single channel measurements. However, use of diversity techniques is known to improve reliability of a propagation channel. It is often seen that diversity measurements lead to conclusions of better signal-to-noise ratio; and thereby reliability specifically meant for data communication. Different diversity techniques, including polarization diversity have been described in “Bluetooth communication employing diversity”; Proc. ISCC, 2003; F. Bektas, B. Vondra, P. E. Veith, L. Faltin, A. Pohl and A. L. Scholtz, for indoor communication set up.
It is thus seen, from FIG. 1 of the accompanying drawings, that there is immense challenge in obtaining a stable RSSI Vs distance profile for indoor environment; in particular having a monotonic behavior. FIG. 1 shows a typical RSSI profile for a ZigBee radio link. The variance in the RSSI values introduces error in the location estimation while the nonmonotonic characteristic gives raise to ambiguity. Since in real life deployment in dense indoor environment, RSSI based distance estimation can lead to multiple distance estimates, there is strong challenge in creating a simple algorithm which will estimate the distance with great accuracy.
Polarization Diversity in Indoors is Discussed Below:
It is known that RSSI can be improved using polarization diversity. As the likelihood is that the signal will suffer some level of attenuation, as it disperses slightly and propagates along fading channel in a given polarization, it is known that propagation characteristics in wireless communication systems are different for vertically and horizontally polarized waves as disclosed in “Spatial, polarization, and pattern diversity for wireless handheld terminals”; Dietrich, C. B., Jr.; Dietze, K.; Nealy, J. R.; Stutzman, W. L.; Antennas and Propagation, IEEE Transactions on Volume 49. Multiple reflections between the transmitter and the receiver lead to depolarization of radio waves, coupling some energy of the transmitted signal into the orthogonal polarized wave. Due to that characteristic of multipath radio channel, vertically/horizontally polarized transmitted waves have also horizontal/vertical component (i.e., additional diversity branch).
A thorough investigation through extensive_experimentations revealed that the packets of deep fading in one polarization often do not coincide with that in other polarization. This phenomenon leads to a reasonable conclusion that in indoor and a RF challenged environment, polarization rotation is a major source of signal attenuation.